Overview

Dataset statistics

Number of variables13
Number of observations5695
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory622.9 KiB
Average record size in memory112.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly overall correlated with volume_products and 5 other fieldsHigh correlation
recency_days is highly overall correlated with purchases and 2 other fieldsHigh correlation
volume_products is highly overall correlated with gross_revenue and 5 other fieldsHigh correlation
assort_products is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
purchases is highly overall correlated with gross_revenue and 6 other fieldsHigh correlation
avg_period_purchases is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
frequency_purchase is highly overall correlated with recency_days and 2 other fieldsHigh correlation
volume_basket_size is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
assort_basket_size is highly overall correlated with assort_products and 2 other fieldsHigh correlation
avg_ticket is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
returns_purchases is highly overall correlated with purchases and 1 other fieldsHigh correlation
returns_products is highly overall correlated with purchases and 1 other fieldsHigh correlation
gross_revenue is highly skewed (γ1 = 21.65488511)Skewed
volume_products is highly skewed (γ1 = 23.05712012)Skewed
volume_basket_size is highly skewed (γ1 = 48.53495052)Skewed
avg_ticket is highly skewed (γ1 = 27.82299041)Skewed
returns_products is highly skewed (γ1 = 51.52432388)Skewed
customer_id has unique valuesUnique
returns_purchases has 4190 (73.6%) zerosZeros
returns_products has 4190 (73.6%) zerosZeros

Reproduction

Analysis started2023-05-11 21:54:59.518637
Analysis finished2023-05-11 21:56:26.911617
Duration1 minute and 27.39 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct5695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16600.708
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:27.131596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12699.1
Q114288.5
median16229
Q318210.5
95-th percentile21731.1
Maximum22709
Range10363
Interquartile range (IQR)3922

Descriptive statistics

Standard deviation2808.2237
Coefficient of variation (CV)0.16916289
Kurtosis-0.82112934
Mean16600.708
Median Absolute Deviation (MAD)1962
Skewness0.4411659
Sum94541034
Variance7886120.5
MonotonicityNot monotonic
2023-05-11T18:56:27.458406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
21110 1
 
< 0.1%
13745 1
 
< 0.1%
15584 1
 
< 0.1%
21089 1
 
< 0.1%
21088 1
 
< 0.1%
21087 1
 
< 0.1%
21086 1
 
< 0.1%
15578 1
 
< 0.1%
12424 1
 
< 0.1%
Other values (5685) 5685
99.8%
ValueCountFrequency (%)
12346 1
< 0.1%
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
ValueCountFrequency (%)
22709 1
< 0.1%
22708 1
< 0.1%
22707 1
< 0.1%
22706 1
< 0.1%
22705 1
< 0.1%
22704 1
< 0.1%
22700 1
< 0.1%
22699 1
< 0.1%
22696 1
< 0.1%
22695 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5449
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1802.4803
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:27.798216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.171
Q1236.135
median612.78
Q31568.67
95-th percentile5323.416
Maximum279138.02
Range279137.6
Interquartile range (IQR)1332.535

Descriptive statistics

Standard deviation7882.9985
Coefficient of variation (CV)4.3734172
Kurtosis610.54599
Mean1802.4803
Median Absolute Deviation (MAD)478.68
Skewness21.654885
Sum10265126
Variance62141665
MonotonicityNot monotonic
2023-05-11T18:56:28.092044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95 9
 
0.2%
4.95 8
 
0.1%
2.95 8
 
0.1%
1.25 8
 
0.1%
1.65 7
 
0.1%
3.75 7
 
0.1%
12.75 7
 
0.1%
4.25 6
 
0.1%
7.5 6
 
0.1%
5.95 6
 
0.1%
Other values (5439) 5623
98.7%
ValueCountFrequency (%)
0.42 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 4
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.25 8
0.1%
1.44 1
 
< 0.1%
1.65 7
0.1%
1.69 1
 
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
136275.72 1
< 0.1%
124564.53 1
< 0.1%
116729.63 1
< 0.1%
91062.38 1
< 0.1%
77183.6 1
< 0.1%
72882.09 1
< 0.1%

recency_days
Real number (ℝ)

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.90694
Minimum0
Maximum373
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:28.394893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122.5
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177.5

Descriptive statistics

Standard deviation111.6299
Coefficient of variation (CV)0.95486123
Kurtosis-0.64357629
Mean116.90694
Median Absolute Deviation (MAD)61
Skewness0.81400758
Sum665785
Variance12461.235
MonotonicityNot monotonic
2023-05-11T18:56:28.692700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 110
 
1.9%
4 105
 
1.8%
3 98
 
1.7%
2 91
 
1.6%
10 86
 
1.5%
8 82
 
1.4%
17 79
 
1.4%
9 79
 
1.4%
7 78
 
1.4%
15 67
 
1.2%
Other values (294) 4820
84.6%
ValueCountFrequency (%)
0 38
 
0.7%
1 110
1.9%
2 91
1.6%
3 98
1.7%
4 105
1.8%
5 52
0.9%
7 78
1.4%
8 82
1.4%
9 79
1.4%
10 86
1.5%
ValueCountFrequency (%)
373 23
0.4%
372 22
0.4%
371 17
0.3%
369 4
 
0.1%
368 13
0.2%
367 16
0.3%
366 15
0.3%
365 19
0.3%
364 11
0.2%
362 7
 
0.1%

volume_products
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1843
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.61387
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:29.014539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1106
median317
Q3805.5
95-th percentile2943.3
Maximum196844
Range196843
Interquartile range (IQR)699.5

Descriptive statistics

Standard deviation4428.7397
Coefficient of variation (CV)4.5255231
Kurtosis785.48718
Mean978.61387
Median Absolute Deviation (MAD)253
Skewness23.05712
Sum5573206
Variance19613735
MonotonicityNot monotonic
2023-05-11T18:56:29.323344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 114
 
2.0%
2 72
 
1.3%
3 51
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 29
 
0.5%
12 25
 
0.4%
88 22
 
0.4%
72 21
 
0.4%
7 20
 
0.4%
Other values (1833) 5257
92.3%
ValueCountFrequency (%)
1 114
2.0%
2 72
1.3%
3 51
0.9%
4 49
0.9%
5 35
 
0.6%
6 29
 
0.5%
7 20
 
0.4%
8 18
 
0.3%
9 7
 
0.1%
10 17
 
0.3%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80179 1
< 0.1%
77373 1
< 0.1%
74215 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%

assort_products
Real number (ℝ)

Distinct436
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.654785
Minimum1
Maximum1785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:29.659148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q113
median36
Q384.5
95-th percentile241.3
Maximum1785
Range1784
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation101.71166
Coefficient of variation (CV)1.4602251
Kurtosis43.866694
Mean69.654785
Median Absolute Deviation (MAD)28
Skewness4.7027614
Sum396684
Variance10345.262
MonotonicityNot monotonic
2023-05-11T18:56:29.958000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 279
 
4.9%
2 148
 
2.6%
3 114
 
2.0%
10 101
 
1.8%
5 99
 
1.7%
9 96
 
1.7%
11 92
 
1.6%
6 92
 
1.6%
8 92
 
1.6%
7 91
 
1.6%
Other values (426) 4491
78.9%
ValueCountFrequency (%)
1 279
4.9%
2 148
2.6%
3 114
2.0%
4 89
 
1.6%
5 99
 
1.7%
6 92
 
1.6%
7 91
 
1.6%
8 92
 
1.6%
9 96
 
1.7%
10 101
 
1.8%
ValueCountFrequency (%)
1785 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
1108 1
< 0.1%
884 1
< 0.1%
816 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%

purchases
Real number (ℝ)

Distinct57
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4691835
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:30.284791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.8029408
Coefficient of variation (CV)1.9609631
Kurtosis301.26194
Mean3.4691835
Median Absolute Deviation (MAD)0
Skewness13.171132
Sum19757
Variance46.280004
MonotonicityNot monotonic
2023-05-11T18:56:30.574647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2871
50.4%
2 827
 
14.5%
3 501
 
8.8%
4 395
 
6.9%
5 236
 
4.1%
6 173
 
3.0%
7 139
 
2.4%
8 98
 
1.7%
9 69
 
1.2%
11 54
 
0.9%
Other values (47) 332
 
5.8%
ValueCountFrequency (%)
1 2871
50.4%
2 827
 
14.5%
3 501
 
8.8%
4 395
 
6.9%
5 236
 
4.1%
6 173
 
3.0%
7 139
 
2.4%
8 98
 
1.7%
9 69
 
1.2%
10 54
 
0.9%
ValueCountFrequency (%)
206 1
< 0.1%
198 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
< 0.1%
60 1
< 0.1%

avg_period_purchases
Real number (ℝ)

Distinct1155
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.66978
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:30.875475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.829167
Q160.633333
median400
Q3400
95-th percentile400
Maximum400
Range399
Interquartile range (IQR)339.36667

Descriptive statistics

Standard deviation167.11895
Coefficient of variation (CV)0.68584191
Kurtosis-1.8360794
Mean243.66978
Median Absolute Deviation (MAD)0
Skewness-0.21922616
Sum1387699.4
Variance27928.742
MonotonicityNot monotonic
2023-05-11T18:56:31.183278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 2923
51.3%
70 21
 
0.4%
46 18
 
0.3%
55 17
 
0.3%
49 16
 
0.3%
31 16
 
0.3%
91 16
 
0.3%
21 15
 
0.3%
35 15
 
0.3%
42 15
 
0.3%
Other values (1145) 2623
46.1%
ValueCountFrequency (%)
1 9
0.2%
2 4
0.1%
2.88372093 1
 
< 0.1%
3 6
0.1%
3.330357143 1
 
< 0.1%
3.351351351 1
 
< 0.1%
4 4
0.1%
4.191011236 1
 
< 0.1%
4.275862069 1
 
< 0.1%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
400 2923
51.3%
366 1
 
< 0.1%
365 1
 
< 0.1%
364 1
 
< 0.1%
363 1
 
< 0.1%
357 2
 
< 0.1%
356 1
 
< 0.1%
355 2
 
< 0.1%
352 1
 
< 0.1%
351 2
 
< 0.1%

frequency_purchase
Real number (ℝ)

Distinct1222
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023062775
Minimum0.0026737968
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:31.516109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0026737968
5-th percentile0.0029673591
Q10.0054495913
median0.012012012
Q30.023798006
95-th percentile0.068799621
Maximum1
Range0.9973262
Interquartile range (IQR)0.018348415

Descriptive statistics

Standard deviation0.048276932
Coefficient of variation (CV)2.0932838
Kurtosis173.37146
Mean0.023062775
Median Absolute Deviation (MAD)0.0075001831
Skewness10.798272
Sum131.3425
Variance0.0023306622
MonotonicityNot monotonic
2023-05-11T18:56:31.836905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01851851852 37
 
0.6%
0.005405405405 32
 
0.6%
0.01639344262 31
 
0.5%
0.004672897196 30
 
0.5%
0.002824858757 30
 
0.5%
0.01538461538 29
 
0.5%
0.05263157895 29
 
0.5%
0.01923076923 28
 
0.5%
0.025 27
 
0.5%
0.04545454545 26
 
0.5%
Other values (1212) 5396
94.7%
ValueCountFrequency (%)
0.002673796791 22
0.4%
0.002680965147 21
0.4%
0.002688172043 17
0.3%
0.002702702703 3
 
0.1%
0.0027100271 13
0.2%
0.002717391304 16
0.3%
0.00272479564 14
0.2%
0.002732240437 19
0.3%
0.002739726027 11
0.2%
0.002754820937 7
 
0.1%
ValueCountFrequency (%)
1 5
0.1%
0.550802139 1
 
< 0.1%
0.5294117647 1
 
< 0.1%
0.5 11
0.2%
0.4 1
 
< 0.1%
0.3333333333 6
0.1%
0.3315508021 1
 
< 0.1%
0.3157894737 1
 
< 0.1%
0.2727272727 2
 
< 0.1%
0.2621621622 1
 
< 0.1%

volume_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2366
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.28332
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:32.153745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median152
Q3290.70833
95-th percentile734.3
Maximum74215
Range74214
Interquartile range (IQR)215.70833

Descriptive statistics

Standard deviation1199.2079
Coefficient of variation (CV)4.4699309
Kurtosis2768.2881
Mean268.28332
Median Absolute Deviation (MAD)97
Skewness48.534951
Sum1527873.5
Variance1438099.6
MonotonicityNot monotonic
2023-05-11T18:56:32.450576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 115
 
2.0%
2 71
 
1.2%
3 51
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 29
 
0.5%
12 26
 
0.5%
72 22
 
0.4%
100 22
 
0.4%
88 21
 
0.4%
Other values (2356) 5254
92.3%
ValueCountFrequency (%)
1 115
2.0%
2 71
1.2%
3 51
0.9%
3.333333333 1
 
< 0.1%
4 49
0.9%
5 35
 
0.6%
5.333333333 1
 
< 0.1%
5.666666667 1
 
< 0.1%
6 29
 
0.5%
6.142857143 1
 
< 0.1%
ValueCountFrequency (%)
74215 1
< 0.1%
40498.5 1
< 0.1%
14149 1
< 0.1%
13956 1
< 0.1%
7824 1
< 0.1%
6009.333333 1
< 0.1%
5963 1
< 0.1%
5196 1
< 0.1%
4300 1
< 0.1%
4282 1
< 0.1%

assort_basket_size
Real number (ℝ)

Distinct1178
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.250387
Minimum0.2
Maximum1108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:32.793379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17.2052632
median15
Q331
95-th percentile173
Maximum1108
Range1107.8
Interquartile range (IQR)23.794737

Descriptive statistics

Standard deviation76.862176
Coefficient of variation (CV)2.0633927
Kurtosis32.882585
Mean37.250387
Median Absolute Deviation (MAD)10
Skewness5.0736237
Sum212140.96
Variance5907.7941
MonotonicityNot monotonic
2023-05-11T18:56:33.082192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 278
 
4.9%
2 160
 
2.8%
3 115
 
2.0%
9 106
 
1.9%
10 105
 
1.8%
7 103
 
1.8%
8 102
 
1.8%
5 102
 
1.8%
6 101
 
1.8%
13 99
 
1.7%
Other values (1168) 4424
77.7%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 7
0.1%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.2%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
1108 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%
703 1
< 0.1%
686 1
< 0.1%
674 1
< 0.1%
673 1
< 0.1%
660 1
< 0.1%
649 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5454
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581.82347
Minimum0.42
Maximum84236.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:33.381021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile12.835
Q1158.9
median296.915
Q3486.1065
95-th percentile1840.001
Maximum84236.25
Range84235.83
Interquartile range (IQR)327.2065

Descriptive statistics

Standard deviation2040.7306
Coefficient of variation (CV)3.5074738
Kurtosis987.91504
Mean581.82347
Median Absolute Deviation (MAD)151.915
Skewness27.82299
Sum3313484.7
Variance4164581.4
MonotonicityNot monotonic
2023-05-11T18:56:33.681852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95 9
 
0.2%
4.95 8
 
0.1%
2.95 8
 
0.1%
1.25 8
 
0.1%
3.75 7
 
0.1%
1.65 7
 
0.1%
12.75 7
 
0.1%
7.5 6
 
0.1%
4.25 6
 
0.1%
5.95 6
 
0.1%
Other values (5444) 5623
98.7%
ValueCountFrequency (%)
0.42 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 4
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.25 8
0.1%
1.44 1
 
< 0.1%
1.65 7
0.1%
1.69 1
 
< 0.1%
ValueCountFrequency (%)
84236.25 1
< 0.1%
77183.6 1
< 0.1%
52940.94 1
< 0.1%
50653.91 1
< 0.1%
21389.6 1
< 0.1%
18745.86 1
< 0.1%
14844.76667 1
< 0.1%
14838.86 1
< 0.1%
13305.5 1
< 0.1%
12681.58 1
< 0.1%

returns_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58841089
Minimum0
Maximum45
Zeros4190
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:33.946720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7505699
Coefficient of variation (CV)2.9750808
Kurtosis190.41518
Mean0.58841089
Median Absolute Deviation (MAD)0
Skewness10.266086
Sum3351
Variance3.0644951
MonotonicityNot monotonic
2023-05-11T18:56:34.181587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4190
73.6%
1 850
 
14.9%
2 289
 
5.1%
3 140
 
2.5%
4 92
 
1.6%
5 37
 
0.6%
6 32
 
0.6%
7 21
 
0.4%
9 8
 
0.1%
11 5
 
0.1%
Other values (13) 31
 
0.5%
ValueCountFrequency (%)
0 4190
73.6%
1 850
 
14.9%
2 289
 
5.1%
3 140
 
2.5%
4 92
 
1.6%
5 37
 
0.6%
6 32
 
0.6%
7 21
 
0.4%
8 5
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
35 1
 
< 0.1%
27 1
 
< 0.1%
21 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
0.1%

returns_products
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct216
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.084284
Minimum0
Maximum80995
Zeros4190
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-05-11T18:56:34.457428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile39
Maximum80995
Range80995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1474.76
Coefficient of variation (CV)31.321704
Kurtosis2718.148
Mean47.084284
Median Absolute Deviation (MAD)0
Skewness51.524324
Sum268145
Variance2174917.1
MonotonicityNot monotonic
2023-05-11T18:56:34.766252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4190
73.6%
1 169
 
3.0%
2 150
 
2.6%
3 105
 
1.8%
4 89
 
1.6%
6 78
 
1.4%
5 61
 
1.1%
12 52
 
0.9%
7 44
 
0.8%
8 43
 
0.8%
Other values (206) 714
 
12.5%
ValueCountFrequency (%)
0 4190
73.6%
1 169
 
3.0%
2 150
 
2.6%
3 105
 
1.8%
4 89
 
1.6%
5 61
 
1.1%
6 78
 
1.4%
7 44
 
0.8%
8 43
 
0.8%
9 41
 
0.7%
ValueCountFrequency (%)
80995 1
< 0.1%
74215 1
< 0.1%
9360 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3331 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%

Interactions

2023-05-11T18:56:13.427130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:01.619218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:07.444159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:12.877836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:19.762732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:26.796178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:32.425960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:38.178409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:44.393602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:49.803869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:56.810692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:03.262727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:08.523371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:13.714985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:02.397098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:07.881703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:13.290599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:20.492777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:27.213766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:32.813906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:38.762483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:44.775107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:50.197518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:57.181462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:03.814110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:08.795489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:13.990829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:02.652955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:08.303590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:13.674366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:21.144710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:27.608232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:33.271602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:39.257430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:45.161336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:50.744565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:57.576280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:04.249620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:09.063349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:14.298104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:02.914826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:08.693767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:14.060649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:21.860153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:28.179225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:33.694434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:39.695974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:45.589758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:51.220217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:57.955458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:04.756778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:09.453128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:14.601916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:03.197646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:09.112613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:14.562147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:22.487589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:28.607838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:34.088962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:40.144858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:46.005750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:51.709569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:58.374644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:05.345970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:09.922858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:14.901738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:03.466402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:09.480745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:15.262655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:23.192390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:28.992824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:34.462094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:40.531667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:46.391802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:52.281010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:58.824002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:05.832635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:10.429549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:15.172603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:03.717255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:09.811670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:15.722953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:23.606761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:29.348361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:34.782112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:40.897250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:46.732606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:52.866292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:59.490751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:06.344511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:10.856302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:15.476873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:04.149799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:10.224812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:16.262724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:24.090930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:29.788910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:35.172882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:41.322772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:47.132010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:53.578115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:00.003637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:06.767168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:11.710721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:15.755030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:04.653929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:10.624902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:16.748193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:24.494725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:30.194634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:35.510695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:42.130444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:47.523285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:54.249362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:00.714223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:07.091530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:12.033495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:16.052832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:05.480120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:11.091223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:17.296292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:24.909661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:30.642762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:36.004750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:42.530283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:48.173406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:54.848119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:01.326501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:07.442785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:12.332322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:16.394640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:05.970399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:11.726938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:18.206293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:25.412707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:31.172686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:36.496013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:42.914044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:48.561304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:55.547635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:01.776201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:07.722562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:12.618168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:16.667094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:06.504687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:12.114116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:18.739417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:25.923795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:31.597625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:37.025520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:43.292295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:48.932075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:55.991507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:02.306903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:07.995359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:12.885441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:16.944935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:06.997414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:12.454945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:19.148920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:26.311574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:31.988405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:37.523031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:43.786724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:49.339903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:55:56.372587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:02.758567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:08.243303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-11T18:56:13.138296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-05-11T18:56:35.054855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
customer_idgross_revenuerecency_daysvolume_productsassort_productspurchasesavg_period_purchasesfrequency_purchasevolume_basket_sizeassort_basket_sizeavg_ticketreturns_purchasesreturns_products
customer_id1.000-0.1810.245-0.291-0.017-0.3830.380-0.215-0.1470.134-0.040-0.274-0.277
gross_revenue-0.1811.000-0.4260.9290.7940.644-0.5700.4030.7220.3700.7650.4400.435
recency_days0.245-0.4261.000-0.496-0.326-0.5960.541-0.872-0.1980.050-0.085-0.330-0.321
volume_products-0.2910.929-0.4961.0000.7340.694-0.6240.4720.7900.2980.6510.4590.458
assort_products-0.0170.794-0.3260.7341.0000.450-0.3830.2910.6180.7330.6390.2800.270
purchases-0.3830.644-0.5960.6940.4501.000-0.9230.5450.157-0.1800.0770.5510.539
avg_period_purchases0.380-0.5700.541-0.624-0.383-0.9231.000-0.565-0.1420.180-0.062-0.487-0.478
frequency_purchase-0.2150.403-0.8720.4720.2910.545-0.5651.0000.194-0.0720.0800.3200.312
volume_basket_size-0.1470.722-0.1980.7900.6180.157-0.1420.1941.0000.5700.8540.1730.183
assort_basket_size0.1340.3700.0500.2980.733-0.1800.180-0.0720.5701.0000.640-0.102-0.102
avg_ticket-0.0400.765-0.0850.6510.6390.077-0.0620.0800.8540.6401.0000.1520.157
returns_purchases-0.2740.440-0.3300.4590.2800.551-0.4870.3200.173-0.1020.1521.0000.987
returns_products-0.2770.435-0.3210.4580.2700.539-0.4780.3120.183-0.1020.1570.9871.000

Missing values

2023-05-11T18:56:19.575835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-11T18:56:20.113774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysvolume_productsassort_productspurchasesavg_period_purchasesfrequency_purchasevolume_basket_sizeassort_basket_sizeavg_ticketreturns_purchasesreturns_products
0178505391.210372.0001733.00021.00034.0001.0000.09150.9710.618158.5651.00040.000
1130473232.59056.0001390.000105.0009.00052.8330.024154.44411.667359.1777.00035.000
2125836705.3802.0005028.000114.00015.00026.5000.040335.2007.600447.0252.00050.000
313748948.25095.000439.00024.0005.00092.6670.01387.8004.800189.6500.0000.000
415100876.000333.00080.0001.0003.00020.0000.00826.6670.333292.0003.00022.000
5152914623.30025.0002102.00061.00014.00026.7690.037150.1434.357330.2365.00029.000
6146885630.8707.0003621.000148.00021.00019.2630.056172.4297.048268.1376.000399.000
7178095411.91016.0002057.00046.00012.00039.6670.032171.4173.833450.9932.00041.000
81531160767.9000.00038194.000567.00091.0004.1910.243419.7146.231667.77927.000474.000
9160982005.63087.000613.00034.0007.00047.6670.01987.5714.857286.5190.0000.000
customer_idgross_revenuerecency_daysvolume_productsassort_productspurchasesavg_period_purchasesfrequency_purchasevolume_basket_sizeassort_basket_sizeavg_ticketreturns_purchasesreturns_products
5752227004839.4201.0001074.00055.0001.000400.0000.5001074.00055.0004839.4200.0000.000
575313298360.0001.00096.0002.0001.000400.0000.50096.0002.000360.0000.0000.000
575414569227.3901.00079.00010.0001.000400.0000.50079.00010.000227.3900.0000.000
57552270417.9001.00014.0007.0001.000400.0000.50014.0007.00017.9000.0000.000
5756227053.3501.0002.0002.0001.000400.0000.5002.0002.0003.3500.0000.000
5757227065699.0001.0001747.000634.0001.000400.0000.5001747.000634.0005699.0000.0000.000
5758227076756.0600.0002010.000730.0001.000400.0001.0002010.000730.0006756.0600.0000.000
5759227083217.2000.000654.00056.0001.000400.0001.000654.00056.0003217.2000.0000.000
5760227093950.7200.000731.000217.0001.000400.0001.000731.000217.0003950.7200.0000.000
576112713794.5500.000505.00037.0001.000400.0001.000505.00037.000794.5500.0000.000